The results above indicate that 5-fold \(\operatorname{CV}_{(1)}\)  is more robust to different error distributions than classical approaches designed for homoscedastic Gaussian errors. We note that there are specialised change-point approaches that tailored to certain model violations: see for instance [1]} and [2]} for methods that are robust to heteroscedasticity and outliers, respectively. However while they may outperform cross-validation used in conjuntion with a standard procedure such as optimal partitioning, we see here that the latter approach performs reasonably well without knowledge of the sort of violation to be expected, without sacrificing too much performance in idealised settings.
